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Journal of Big Data — Template for authors

Publisher: Springer

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Guideline source

Categories	Rank	Trend in last 3 yrs
Information Systems	#30 of 329	down by 5 ranks
Information Systems and Management	#13 of 125	down by 3 ranks
Computer Networks and Communications	#34 of 334	down by 11 ranks
Hardware and Architecture	#18 of 157	down by 5 ranks

Journal quality:

High

Last 4 years overview: 316 Published Papers | 2714 Citations

Indexed in: Scopus

Last updated: 10/06/2020

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General info

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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

8.6

41% from 2019

CiteRatio for Journal of Big Data from 2016 - 2020
Year	Value
2020	8.6
2019	6.1
2018	10.6
2017	7.3
2016	4.6

Graph view

1.031

11% from 2019

SJR for Journal of Big Data from 2016 - 2020
Year	Value
2020	1.031
2019	0.925
2018	1.124
2017	1.143
2016	0.83

Graph view

3.478

39% from 2019

SNIP for Journal of Big Data from 2016 - 2020
Year	Value
2020	3.478
2019	2.501
2018	4.512
2017	4.649
2016	4.885

Graph view

Insights

CiteRatio of this journal has increased by 41% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

Insights

SJR of this journal has increased by 11% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has increased by 39% in last years.
This journal’s SNIP is in the top 10 percentile category.

Journal of Big Data

Guideline source: View

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Springer

Journal of Big Data

The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. Read Less

Communications Engineering, Networks

Information Systems and Management

Computer Networks and Communications

Hardware and Architecture

Decision Sciences

Last updated on	10 Jun 2020
ISSN	1606-8610
Impact Factor	Very Low - 0.024
Acceptance Rate	Not provided
Frequency	Not provided
Open Access	No
Sherpa RoMEO Archiving Policy	White
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	SPBASIC
Citation Type	Author Year (Blonder et al, 1982)
Bibliography Example	Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515–4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Open access • Journal Article • DOI: 10.1186/S40537-019-0197-0 •

A survey on Image Data Augmentation for Deep Learning

Connor Shorten¹, Taghi M. Khoshgoftaar¹ •

06 Jul 2019 - Journal of Big Data

Abstract:

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunatel... Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data. read more

Topics:

Overfitting (56%)56% related to the paper, Deep learning (55%)55% related to the paper, Big data (52%)52% related to the paper,

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5,782 Citations

Open access • Journal Article • DOI: 10.1186/S40537-016-0043-6 •

A survey of transfer learning

Karl R. Weiss¹, Taghi M. Khoshgoftaar¹, •

28 May 2016 - Journal of Big Data

Abstract:

Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in som... Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. read more

Topics:

Active learning (machine learning) (68%)68% related to the paper, Inductive transfer (67%)67% related to the paper, Instance-based learning (66%)66% related to the paper,

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2,900 Citations

Open access • Journal Article • DOI: 10.1186/S40537-014-0007-7 •

Deep learning applications and challenges in big data analytics

Maryam M. Najafabadi¹, Flavio Villanustre², •

24 Feb 2015 - Journal of Big Data

Abstract:

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud dete... Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning. read more

Topics:

Analytics (69%)69% related to the paper, Big data (67%)67% related to the paper, Business intelligence (59%)59% related to the paper,

View PDF

1,827 Citations

Open access • Journal Article • DOI: 10.1186/S40537-019-0192-5 •

Survey on deep learning with class imbalance

Justin M. Johnson¹, Taghi M. Khoshgoftaar¹ •

01 Mar 2019 - Journal of Big Data

Abstract:

The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, hi... The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research. read more

Topics:

Feature learning (55%)55% related to the paper, Deep learning (55%)55% related to the paper, Class (computer programming) (52%)52% related to the paper,

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1,377 Citations

Open access • Journal Article • DOI: 10.1186/S40537-021-00444-8 •

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi¹, Jinglan Zhang¹, •

01 Jan 2021 - Journal of Big Data

Abstract:

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even be... In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion. read more

Topics:

Deep learning (51%)51% related to the paper

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1,084 Citations

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Frequently asked questions

1. Can I write Journal of Big Data in LaTeX?

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3. Can I cite my article in multiple styles in Journal of Big Data?

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12. Is Journal of Big Data's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Big Data?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Big Data. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Big Data?

The 5 most common citation types in order of usage for Journal of Big Data are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Big Data Endnote style according to Elsevier guidelines.

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Journal of Big Data — Template for authors

Related Journals

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

8.6

1.031

3.478

Insights

Insights

Insights

Journal of Big Data

Journal of Big Data

Top papers written in this journal

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